Sharing temporary information across computers in a data center introduces a bottleneck in distributed workflows. For example, an application on one computer may produce temporary data consumed by an application on another computer within, e.g., a cluster of computers in a data center. The computer producing the temporary data waits until all the temporary data is transferred to the consumer computer, or at least in a buffer which will be transferred to the consumer computer, before continuing. The consumer computer waits for the data to be in a state which it can consume. This type of sharing of temporary information can be achieved using shared file systems or specialized protocols. Shared file systems, however, introduce undesirable overhead and specialized protocols are complex to program and often preclude the use of applications developed prior to or without implementation of the protocols. For example, Hadoop Distributed File System (HDFS) is a common mechanism used to transfer temporary state in a distributed system. HDFS, however, suffers from significant overhead to ensure fault-tolerance and high-availability. Other systems may provide a memory interface over remote direct memory access (RDMA). That, however, requires rewriting the software that expects to export data to temporary files to be compliant with an RDMA protocol.